Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings

Gutiérrez, José Antonio¹  Quirós, Elia²  Mora, Julián³  Fragoso-Campón, Laura⁴  Durán-Barroso, Pablo⁵ 

[1] Author to whom correspondence should be addressed.;Department of Art and Territorial Sciences, Universidad de Extremadura, 10003 Caceres, Spain;Department of Construction, Universidad de Extremadura, 10003 Caceres, Spain;Department of Graphic Expression, Universidad de Extremadura, 10003 Caceres, Spain;Presented at Environment, Green Technology and Engineering International Conference (EGTEIC 2018), Caceres, Spain, 18–20 June 2018.

关键词: remote sensing;    Sentinel 2A multispectral imagery;    forest l;    cover;    multitemporal analysis;    LiDAR;    r;    om forest;   

DOI  :  10.3390/proceedings2201280

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

【 授权许可】

CC BY